Embodiments of the present invention are related to digital image processing and, in particular, to blind image deconvolution.
Convolution refers to a mathematical operation by which two functions are combined to form a third function. Deconvolution refers to reversing the effects of convolution. Deconvolution is commonly used in image processing applications to remove the effects of distortion in an image. In image processing, a preprocessed image can be thought of as the convolution of a first function, i.e., the sharp image, and a second function, i.e., a point spread function (PSF) which introduces distortion to the preprocessed image. Deconvolution of the preprocessed image enables the underlying sharp image to be separated from the distortion introduced by the PSF, improving image quality. In images where atmospheric turbulence is the primary source of image distortion, the PSF can refer to the atmospheric PSF.
Blind deconvolution refers to the idea that the PSF is unknown. To effectively remove the distortion introduced by the unknown PSF, the unknown PSF needs to be estimated or modeled. Prior art methods exist for automatically computing the point spread function from raw video data for use in a deconvolution algorithm. However, these methods are typically very computationally intensive, for example involving extensive iterations or complex algorithms. These previous techniques require substantial computation time and resources. This increases system resource requirements and power consumption, making it more difficult to incorporate these techniques into portable applications.
Embodiments of the present invention address these and other problems.